mlsanityData to 7 Oct 2026

Comparative Analysis of Personalized Voice Activity Detection Systems: Assessing Real-World Effectiveness

arXiv 2406.09443 ↗First posted 12 Jun 2024eess.AS cs.HC cs.LGcited by 1

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Abstract (arXiv)

Voice activity detection (VAD) is a critical component in various applications such as speech recognition, speech enhancement, and hands-free communication systems. With the increasing demand for personalized and context-aware technologies, the need for effective personalized VAD systems has become paramount. In this paper, we present a comparative analysis of Personalized Voice Activity Detection (PVAD) systems to assess their real-world effectiveness. We introduce a comprehensive approach to assess PVAD systems, incorporating various performance metrics such as frame-level and utterance-level error rates, detection latency and accuracy, alongside user-level analysis. Through extensive experimentation and evaluation, we provide a thorough understanding of the strengths and limitations of various PVAD variants. This paper advances the understanding of PVAD technology by offering insights into its efficacy and viability in practical applications using a comprehensive set of metrics.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 19 of its 32 references that are in the map. First posted on arXiv 12 Jun 2024.

Builds on

19 of 32 references.

References · 19

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7 more
  1. 2022Target-Speaker Voice Activity Detection via Sequence-to-Sequence Predictioncited by 6
  2. 2021Enrollment-less training for personalized voice activity detectioncited by 4
  3. 2022Cross-Channel Attention-Based Target Speaker Voice Activity Detection: Experimental Results for M2MeT Challengecited by 3
  4. 2023Efficient Multimodal Neural Networks for Trigger-less Voice Assistantscited by 3
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Citing papers per year: 2025: 1.

1 paper.

  1. 2025HyWA: Architecture-Preserving Personalized Voice Activity Detection for Full-Duplex Voice Assistantscited by 0